Challenges in the use of Near Infrared Spectroscopy for improving wood quality: A review

Paulo R. G. Hein, Hannu K. Pakkanen, António A. Dos Santos


Aims of study: Forestry-related companies require quality monitoring methods capable to pass a large number of samples. This review paper is dealing with the utilization of near infrared (NIR) technique for wood analysis.

Area of study: We have a global point of view for NIR applications and characterization of different kind of wood species is considered.

Material and methods: NIR spectroscopy is a fast, non-destructive technique, applicable to any biological material, demanding little or no sample preparation. NIR spectroscopy and multivariate analysis serve well in laboratories where the conditions are controlled. The main challenges to NIR spectroscopy technique in field conditions are moisture content and portability.

Results: In this review, the methods and challenges for successfully applying NIR spectroscopy in the field of wood characterization are presented. Portable equipment need to record NIR spectra with low noise and low sensitivity to temperature and humidity variations of the air in forest environments. Studies concerning the sample preparation effects on the robustness of the calibrations are thus required.

Research highlights: This paper examines traditional applications and practical aspects as well as innovative modern adaptations applied, for example, in hyperspectral imaging and genetic studies.


Near Infrared Spectroscopy; wood properties; moisture; pulp; camera hyperspectral, genetic studies

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Abasolo M, Lee DJ, Raymond C, Meder R, Shepherd M, 2013. Deviant near-infrared spectra allows identification of Corymbia hybrids. Forest Ecol Manag 304: 121-131.

Adedipe OE, Dawson-Andoh B, 2008. Predicting moisture content of yellow-poplar (Liriodendron tulipifera) veneer using near infrared spectroscopy. For Prod J 58: 28-33.

Birkett MD, Gambino MJT, 1989. Estimation of pulp kappa number with near infrared spectroscopy. Tappi J 72: 193-197.

Bokobza L, 1998. Near infrared spectroscopy. J Near Infrared Spectrosc 6: 3-17.

Brawner JT, Meder R, Lee DJ, Dieters M, 2012. Selection of Corymbia citriodora for pulp productivity. South Forests 74: 121-131.

Brereton RG, 2003. Chemometrics: data analysis for the laboratory and chemical plant. John Wiley & Sons Ltd, Chichester, England. 489 pp.

Cogdill RP, Schimleck LR, Jones PD, Peter GF, Daniels RF, Clark A, 2004. Estimation of the physical wood properties of Pinus taeda L radial strips using least square support vector machines. J Near Infrared Spectrosc 12: 263-269.

Cooper PA, Jeremic D, Radivojevic S, Ung YT, Leblon B, 2011. Potential of near-infrared spectroscopy to characterize wood products. Can J For Res 41: 2150-2157.

Defo M, Taylor M, Bond B, 2007. Determination of moisture content and density of fresh-sawn red oak lumber by near-infrared spectroscopy. For Prod J 57: 68-72.

Downes GM, Meder R, Ebdon N, Bond H, Evans R, Joyce K, Southerton S, 2010. Radial variation in cellulose content and Kraft pulp yield in Eucalyptus nitens using near-infrared spectral analysis of air-dry wood surfaces. J Near Infrared Spectrosc 18: 147-155.

Duncker P, Spiecker H, 2009. Detection and classification of Norway spruce compression wood in reflected light by means of hyperspectral image analysis. IAWA J 30: 59-70.

Easty DB, Berben SA, DeThomas FA, Brimmer PJ, 1990. Near-infrared spectroscopy for the analysis of wood pulp: quantifying hardwood-softwood mixtures and estimating lignin content. Tappi J 73: 257-261.

Estopa RA, Milagres FR, Oliveira RA, Hein PRG, 2017. NIR spectroscopic models for phenotyping wood traits in breeding programs of Eucalyptus benthamii. Cerne 22: 367-375.

Fujimoto T, Kurata Y, Matsumoto K, Tsuchikawa S, 2008. Application of near infrared spectroscopy for estimating wood mechanical properties of small clear and full length lumber specimens. J Near Infrared Spectrosc 16: 529-537.

Gierlinger N, Schwanninger M, Hinterstoisser B, Wimmer R, 2002. Rapid determination of heartwood extractives in Larix sp by means of Fourier transform near infrared spectroscopy. J Near Infrared Spectrosc 10: 203-214.

Glass SV, Zelinka SL, 2010. Moisture relations and physical properties of wood. In: Wood handbook - Wood as an engineering material, Chapter 4. Centennial ed, general technical report FPL-GTR-190. Pp: 1-19 USDA Forest Service Forest Products Laboratory, Madison.

Greaves BL, Schimleck LR, Borralho NMG, Michell AJ, 1996. Genetic control and repeatability of near infrared reflectance from Eucalyptus nitens woodmeal. Appita J 49: 423-426.

Haddadi A, Hans G, Leblon B, Pirouz Z, Tsuchikawa S, Naderd J, Groves K, 2016. Determination of optical parameters and moisture content of wood with visible-near infrared spectroscopy. J Near Infrared Spectrosc 24: 571-585.

Hans G, Leblon B, Stirling R, Nader J, LaRocque A, Cooper P, 2012. Monitoring of moisture content and basic specific gravity in black spruce logs using a handheld MEMS-based near-infrared spectrometer. The Forestry Chronicle 89: 605-618.

Hein PRG, Chaix G, 2014. NIR spectral heritability: a promising tool for wood breeders? J Near Infrared Spectrosc 22: 141-147.

Hein PRG, Lima JT, Chaix G, 2009. Robustness of models based on near infrared spectra to predict the basic density in Eucalyptus urophylla wood. J Near Infrared Spectrosc 17: 141-150.

Hein PRG, Bouvet JM, Mandrou E, Vigneron P, Clair B, Chaix G, 2012. Age trends of microfibril angle inheritance and their genetic and environmental correlations with growth density and chemical properties in Eucalyptus urophylla ST Blake wood. Ann For Sci 69: 681-691.

Hoffmeyer P, Pedersen JG, 1995. Evaluation of density and strength of Norway spruce wood by near-infrared reflectance spectroscopy. Holz als Roh- und Werkstoff 53: 165-170.

Hung TD, Brawner JT, Meder R, Lee DJ, Southerton SG, Thinhand HH, Dieters MJ, 2015. Estimates of genetic parameters for growth and wood properties of Eucalyptus pellita F Muell to support tree breeding in Vietnam. Ann Forest Sci 72: 205-217.

Kelley SS, Rials TG, Snell R, Groom LH, Sluiter A, 2004. Use of near infrared spectroscopy to measure the chemical and mechanical properties of solid wood. Wood Sci Technol 38: 257-276.

Kobori H, Gorretta N, Rabatel G, Bellon-Maurel V, Chaix G, Roger JM, Tsuchikawa S, 2013. Applicability of Vis-NIR hyperspectral imaging for monitoring wood moisture content (MC). Holzforschung 67: 307-314.

Leblon B, Adedipe O, Hans G, Haddadi A, Tsuchikawa A, Burger J, Stirling R, Pirouz Z, Groves K, Nader J, LaRocque A, 2013. A review of near-infrared spectroscopy for monitoring moisture content and density of solid wood. The Forestry Chronicle 89: 595-606.

Lepoittevin C, Rousseau JP, Guillemin A, Gauvrit C, Besson F, Hubert F, da Silva Perez D, Harvengt L, Plomion C, 2011. Genetic parameters of growth straightness and wood chemistry traits in Pinus pinaster. Ann For Sci 68: 873-884.

Mandrou E, Hein PRG, Villar E, Vigneron P, Plomion C, Gion JM, 2012. A candidate gene for lignin composition in Eucalyptus: cinnamoyl-CoA reductase (CCR). Tree Genet Genom 8: 353-364.

Manley M, 2014. Near-infrared spectroscopy and hyperspectral imaging: non-destructive analysis of biological materials. Chem Soc Rev 43: 8200-8214.

Mark H, Workman J, 2007. Chemometrics in spectroscopy. Academic Press, London, UK. 526 pp.

Meder R, 2015. The magnitude of tree breeding and the role of near infrared spectroscopy. NIR News 26: 8-10.

Meder R, Brawner JT, Downes GM, Ebdon N, 2011. Towards the in-forest assessment of Kraft pulp yield: comparing the performance of laboratory and hand-held instruments and their value in screening breeding trials. J Near Infrared Spectrosc 19: 421-429.

Meder R, Meglen R, 2012. Near infrared spectroscopic and hyperspectral imaging of compression wood in Pinus radiata D Don. J Near Infrared Spectrosc 20: 583-589.

Meder R, Kain D, Ebdon N, Macdonell P, Brawner JT, 2014. Identifying hybridisation in Pinus species using NIR spectroscopy of foliage. J Near Infrared Spectrosc 22: 337-345.

Mora CR, Schimleck LR, Isik F, 2008. Near infrared calibration models for the estimation of wood density in Pinus taeda using repeated sample measurement. J Near Infrared Spectrosc 16: 517-528.

Mora CR, Schimleck LR, Clark A, Daniels RF, 2011a. Determination of basic density and moisture content of merchantable loblolly pine logs by near-infrared spectroscopy. J Near Infrared Spectrosc 19: 391-399.

Mora CR, Schimleck LR, Yoon S-C, Thai CN, 2011b. Determination of basic density and moisture content of loblolly pine wood disks using a near-infrared hyperspectral imaging system. J Near Infrared Spectrosc 19: 401-409.

Næs T, Isaksson T, Fearn T, Davies T, 2002. A user-friendly guide to multivariate calibration and classification. NIR Publications, Chichester, UK. 344 pp.

Pasquini C, 2003. Near infrared spectroscopy: fundamentals, practical aspects and analytical applications. J Braz Chem Soc 14: 198-219.

Pereira H, Santos AJA, Anjos O, 2016. Fibre morphological characteristics of Kraft pulps of Acacia melanoxylon estimated by NIR-PLS-R models. Materials 9 (1): 8.

Posada H, Ferrand M, Davrieux F, Lashermes P, Bertrand B, 2009. Stability across environments of the coffee variety near infrared spectral signature. Heredity 102: 113-119.

Raymond CA, 2002. Genetics of Eucalyptus wood properties. Ann For Sci 59: 525-531.

Rodrigues J, Alves A, Pereira H, da Silva Perez D, Chantre G, Schwanninger M, 2006. NIR PLSR results obtained by calibration with noisy low-precision reference values: Are the results acceptable? Holzforschung 60: 402-408.

Sandak A, Sandak J, Böhm K, Zitek A, Hinterstoisser B, 2016. Near infrared spectroscopy as a tool for in-field determination of log/biomass quality index in mountain forests. J Near Infrared Spectrosc 24: 587-594.

Sandak J, Sandak A, Meder R, 2016. Tutorial - Assessing trees wood and derived products with near infrared spectroscopy: hints and tips. J Near Infrared Spectrosc 24: 485-505.

Santos AJA, Alves AMM, Simões RMS, Pereira H, Rodrigues J, Schwanninger M, 2012. Estimation of wood basic density of Acacia melanoxylon (R Br) by near infrared spectroscopy. J Near Infrared Spectrosc 20: 267-274.

Santos AJA, Anjos O, Pereira H, 2015. Estimation of Acacia melanoxylon unbleached Kraft pulp brightness by NIR spectroscopy. Forest Syst 24 (2): eRC03.

Schimleck LR, 2008. Near-infrared spectroscopy: A rapid non-destructive method for measuring wood properties and its application to tree breeding. N Z J For Sci 38: 14-35.

Schimleck LR, Raymond CA, Beadle CL, Downes GM, Kube PD, French J, 2000. Applications of NIR spectroscopy to forest research. Appita J 53: 458-464.

Schimleck LR, Evans R, Ilic J, 2001. Estimation of Eucalyptus delegatensis wood properties by near infrared spectroscopy. Can J For Res 31: 1671-1675.

Schwanninger M, Rodrigues JC, Fackler K, 2011. A review of band assignments in near infrared spectra of wood and wood components. J Near Infrared Spectrosc 19: 287-308.

Silva Perez D, Guillemain A, Alazard P, Plomion C, Rozenberg P, Rodrigues JC, Alves A, Chantre G, 2007. Improvement of Pinus pinaster Ait elite trees selection by combining near infrared spectroscopy and genetic tools. Holzforschung 61: 611-622.

Smeland KA, Liland KH, Sandak J, Sandak A, Gobakken LR, Thiis TK, Burud I, 2016. Near infrared hyperspectral imaging in transmission mode: assessing the weathering of thin wood samples. J Near Infrared Spectrosc 24: 595-604.

So CL, Via B, Groom LH, Schimleck LR, Shupe TF, Kelley SS, Rials TG, 2004. Near-infrared spectroscopy in the forest products industry. For Prod J 54 (3): 6-16.

Sousa-Correia C, Alves A, Rodrigues JC, Ferreira-Dias S, Abreu JM, Maxted N, Ford-Lloyd B, Schwanninger M, 2007. Oil content estimation of individuals kernels of Quercus ilex subsp rotundifolia [(Lam) O Schwarz] acorns by Fourier transform near infrared spectroscopy and partial least squares regression. J Near Infrared Spectrosc 15: 247-260.

Tatzer P, Wolf M, Panner T, 2005. Industrial application for inline material sorting using hyperspectral imaging in the NIR range Real-Time. Imaging 11: 99-107.

Thumm A, Riddell M, Nanayakkara B, Harrington J, Meder R, 2010. Near infrared hyperspectral imaging applied to mapping chemical composition in wood samples. J Near Infrared Spectrosc 18: 507-515.

Thygesen L, 1994. Determination of dry matter content and basic density of Norway spruce by near-infrared reflectance and transmittance spectroscopy. J Near Infrared Spectrosc 2: 127-135.

Thygesen LG, Lundqvist SO, 2000. NIR measurement of moisture content in wood under unstable temperature conditions Part 2 Handling temperature fluctuations. J Near Infrared Spectrosc 8: 191-199.

Tsuchikawa S, 2007. A review of recent near infrared research for wood and paper. Appl Spectrosc Rev 42: 43-71.

Tsuchikawa S, Schwanninger M, 2013. A review of recent near-infrared research for wood and paper (Part 2). Appl Spectrosc Rev 48: 560-587.

Tsuchikawa S, Kobori H, 2015. A review of recent application of near infrared spectroscopy to wood science and technology. J Wood Sci 61: 213-220.

Tsuchikawa S, Hayashi K, Tsutsumi S, 1992. Application of near infrared spectrophotometry to wood 1 Effects of the surface-structure. Mokuzai Gakkaishi 38: 128-136.

Verryn SD, 2008. Breeding for wood quality - A perspective for the future. N Z J For Sci 38: 5-13.

Via BK, Shupe TF, Groom LH, Stine M, So CL, 2003. Multivariate modelling of density strength and stiffness from near infrared spectra for mature juvenile and pith wood of longleaf pine (Pinus palustris). J Near Infrared Spectrosc 11: 365-378.

Via BK, So CL, Shupe TF, Stine M, Groom LH, 2005. Ability of near infrared spectroscopy to monitor air-dry density distribution and variation of wood. Wood Fiber Sci 37: 394-402.

Wallbäcks L, Edlund U, Norden B, Berglund I, 1991. Multivariate characterization of pulp using 13C NMR FTIR and NIR. Tappi J 74: 201-206.

Watanabe K, Hart JF, Mansfield SD, Avramidis S, 2010. Near infrared technology applications for quality control in wood processing. Proc COST E53 Conf, Edinburgh (UK), pp: 332-341.

Watanabe K, Mansfield SD, Avramidis S, 2011. Application of near-infrared spectroscopy for moisture-based sorting of green hem-fir timber. J Wood Sci 57: 288-294.

Wright JA, Birkett MD, Gambino MJT, 1990. Prediction of pulp yield and cellulose content from wood samples using near infrared reflectance spectroscopy. Tappi J 73: 164-166.

DOI: 10.5424/fs/2017263-11892